Knowledgebase: LANCE
FIRMS Fire Confidence Value
Posted by Diane Davies, Last modified by Diane Davies on 17 May 2018 08:20 AM


The confidence value was added to help users gauge the quality of individual fire pixels is included in the Level 2 fire product. They are different for MODIS and VIIRS. 

For MODIS the confidence value ranges between 0% and 100% and can be used to assign one of the three fire classes (low-confidence fire, nominal-confidence fire, or high-confidence fire) to all fire pixels within the fire mask. In some applications errors of commission (or false alarms) are particularly undesirable, and for these applications one might be willing to trade a lower detection rate to gain a lower false alarm rate. Conversely, for other applications missing any fire might be especially undesirable, and one might then be willing to tolerate a higher false alarm rate to ensure that fewer true fires are missed. Users requiring fewer false alarms may wish to retain only nominal- and high-confidence fire pixels, and treat low-confidence fire pixels as clear, non-fire, land pixels. Users requiring maximum fire detectability who are able to tolerate a higher incidence of false alarms should consider all three classes of fire pixels.

For VIIRS: This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.

The confidence value is application specific.  This isn't very helpful, I know, but unfortunately there's no way to establish an optimal cutoff a priori.  Users have to adopt an empirical approach -- what threshold works best for what I'm trying to do?  Unfortunately the confidence values in the product do not directly correspond to the statistical confidence levels in reference to Type I and Type II errors.

For more information please see the FAQs ( and the User Guides (

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